An improving SRL model with word sense information using an improved synergetic neural network model

Abstract

As a fundamental task of natural language processing, semantic role labeling (SRL) have attracted much attention of researchers in recent years. However, with increasing features being added into the studies, the performance growth trend of SRL is gradually slowing down. So new ways must be found to improve the performance of semantic analysis. Word sense information is useful for SRL task. But how to effectively make use of word sense information is a key issue. Referring to synergetics, we can regard semantic analysis process as competitive process of many semantics order parameters under coherent action and interactive collaboration of semantic role-related features and word sense-related features. Accordingly, we propose a semantic role labeling model with word sense information based on improved synergetic neural network (SNN). Our contributions are three-fold. Firstly, role-related features and word sense-related features are used to configure semantic order parameters of SNN. Secondly, network parameters are reconstructed which can reflect the relationship of driving and restraining each other between various linguistic features. Finally, we use an improved quantum particle swarm algorithm (QPSO) to realize the optimization of network parameter which has stronger search ability and faster convergence speed. By evaluating our model on the OntoNotes 2.0 corpus, the experiment results show the proposed model in this paper leads to a higher performance for SRL.